diff --git a/GPy/core/gp.py b/GPy/core/gp.py
index 115c8d0f..7b67e722 100644
--- a/GPy/core/gp.py
+++ b/GPy/core/gp.py
@@ -142,7 +142,7 @@ class GP(GPBase):
 
         """
         # normalize X values
-        Xnew = (Xnew.copy() - self._Xmean) / self._Xstd
+        Xnew = (Xnew.copy() - self._Xoffset) / self._Xscale
         mu, var = self._raw_predict(Xnew, full_cov=full_cov, which_parts=which_parts)
 
         # now push through likelihood
diff --git a/GPy/core/gp_base.py b/GPy/core/gp_base.py
index 9c7e4a9e..0799e0c2 100644
--- a/GPy/core/gp_base.py
+++ b/GPy/core/gp_base.py
@@ -21,12 +21,12 @@ class GPBase(Model):
         self.num_data, self.output_dim = self.likelihood.data.shape
 
         if normalize_X:
-            self._Xmean = X.mean(0)[None, :]
-            self._Xstd = X.std(0)[None, :]
-            self.X = (X.copy() - self._Xmean) / self._Xstd
+            self._Xoffset = X.mean(0)[None, :]
+            self._Xscale = X.std(0)[None, :]
+            self.X = (X.copy() - self._Xoffset) / self._Xscale
         else:
-            self._Xmean = np.zeros((1, self.input_dim))
-            self._Xstd = np.ones((1, self.input_dim))
+            self._Xoffset = np.zeros((1, self.input_dim))
+            self._Xscale = np.ones((1, self.input_dim))
 
         super(GPBase, self).__init__()
         # All leaf nodes should call self._set_params(self._get_params()) at
@@ -107,7 +107,7 @@ class GPBase(Model):
 
         if self.X.shape[1] == 1:
 
-            Xu = self.X * self._Xstd + self._Xmean # NOTE self.X are the normalized values now
+            Xu = self.X * self._Xscale + self._Xoffset # NOTE self.X are the normalized values now
 
             Xnew, xmin, xmax = x_frame1D(Xu, plot_limits=plot_limits)
             m, _, lower, upper = self.predict(Xnew, which_parts=which_parts)
diff --git a/GPy/core/sparse_gp.py b/GPy/core/sparse_gp.py
index c1aed5d5..df3160f1 100644
--- a/GPy/core/sparse_gp.py
+++ b/GPy/core/sparse_gp.py
@@ -43,11 +43,11 @@ class SparseGP(GPBase):
             self.X_variance = X_variance
 
         if normalize_X:
-            self.Z = (self.Z.copy() - self._Xmean) / self._Xstd
+            self.Z = (self.Z.copy() - self._Xoffset) / self._Xscale
 
         # normalize X uncertainty also
         if self.has_uncertain_inputs:
-            self.X_variance /= np.square(self._Xstd)
+            self.X_variance /= np.square(self._Xscale)
 
     def _compute_kernel_matrices(self):
         # kernel computations, using BGPLVM notation
@@ -269,9 +269,9 @@ class SparseGP(GPBase):
 
         """
         # normalize X values
-        Xnew = (Xnew.copy() - self._Xmean) / self._Xstd
+        Xnew = (Xnew.copy() - self._Xoffset) / self._Xscale
         if X_variance_new is not None:
-            X_variance_new = X_variance_new / self._Xstd ** 2
+            X_variance_new = X_variance_new / self._Xscale ** 2
 
         # here's the actual prediction by the GP model
         mu, var = self._raw_predict(Xnew, X_variance_new, full_cov=full_cov, which_parts=which_parts)
@@ -292,13 +292,13 @@ class SparseGP(GPBase):
         GPBase.plot(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20, ax=ax)
         if self.X.shape[1] == 1:
             if self.has_uncertain_inputs:
-                Xu = self.X * self._Xstd + self._Xmean  # NOTE self.X are the normalized values now
+                Xu = self.X * self._Xscale + self._Xoffset  # NOTE self.X are the normalized values now
                 ax.errorbar(Xu[which_data, 0], self.likelihood.data[which_data, 0],
                             xerr=2 * np.sqrt(self.X_variance[which_data, 0]),
                             ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
-            Zu = self.Z * self._Xstd + self._Xmean
+            Zu = self.Z * self._Xscale + self._Xoffset
             ax.plot(Zu, np.zeros_like(Zu) + ax.get_ylim()[0], 'r|', mew=1.5, markersize=12)
 
         elif self.X.shape[1] == 2:
-            Zu = self.Z * self._Xstd + self._Xmean
+            Zu = self.Z * self._Xscale + self._Xoffset
             ax.plot(Zu[:, 0], Zu[:, 1], 'wo')